Journal of Medical Engineering and Technology最新文献

This study presents a solid approach for small-scale medical oxygen production unit using pressure swing adsorption (PSA) technology. The objective of this research is to develop a mathematical model and conduct a sensitivity analysis to optimise the design and operating parameters of the PSA system. Based on the simulation results, an optimal set of operational parameter values has been obtained for the PSA beds. The result shows that the binary system produced oxygen with a purity of 94%, at the adsorption pressure 1 bar and temperature of 308K. The findings demonstrate the effectiveness of the proposed small-scale PSA system for medical oxygen production, highlighting the impact of key parameters and emphasising the need for careful optimisation. The findings serve as a guide for the design and operation of small-scale PSA systems, enabling healthcare facilities to produce their own medical oxygen, thereby improving accessibility and addressing critical shortages during emergencies. Future research may explore the integration of large scale PSA units in hospitals in Morocco.

Visual inspection is the typical way for evaluating burns, due to the rising occurrence of burns globally, visual inspection may not be sufficient to detect skin burns because the severity of burns can vary and some burns may not be immediately apparent to the naked eye. Burns can have catastrophic and incapacitating effects and if they are not treated on time can cause scarring, organ failure, and even death. Burns are a prominent cause of considerable morbidity, but for a variety of reasons, traditional clinical approaches may struggle to effectively predict the severity of burn wounds at an early stage. Since computer-aided diagnosis is growing in popularity, our proposed study tackles the gap in artificial intelligence research, where machine learning has received a lot of attention but transfer learning has received less attention. In this paper, we describe a method that makes use of transfer learning to improve the performance of ML models, showcasing its usefulness in diverse applications. The transfer learning approach estimates the severity of skin burn damage using the image data of skin burns and uses the results to improve future methods. The DL technique consists of a basic CNN and seven distinct transfer learning model types. The photos are separated into those displaying first, second, and third-degree burns as well as those showing healthy skin using a fully connected feed-forward neural network. The results demonstrate that the accuracy of 93.87% for the basic CNN model which is significantly lower, with the VGG-16 model achieving the greatest accuracy at 97.43% and being followed by the DenseNet121 model at 96.66%. The proposed approach based on CNN and transfer learning techniques are tested on datasets from Kaggle 2022 and Maharashtra Institute of Technology open-school medical repository datasets that are clubbed together. The suggested CNN-based approach can assist healthcare professionals in promptly and precisely assessing burn damage, resulting in appropriate therapies and greatly minimising the detrimental effects of burn injuries.

Phonocardiogram signal (PCG) has been the subject of several signal processing studies, where researchers applied various analysis techniques and extracted numerous features for different purposes, like cardiac pathologies identification, healthy/pathologic case discrimination, and severity assessment. When talking about cardiac severity, many think directly about the intensity or energy of the signal as the most reliable parameter. However, cardiac severity is not always reflected by the intensity or energy of the signal but includes other variables as well. In this paper, we will discuss the probability of having a Discrete Wavelet Transform (DWT) parameter that discriminates, identifies, and assesses the pathological cardiac severity levels, a parameter that takes into consideration other variables and elements for the severity study. For this purpose, we studied six PCGs signals that contain reduced murmurs (clicks) and eight murmur signals with four different cardiac severity levels. We extracted the Entropy of Approximation Coefficients (EAC) from the Discrete Wavelet Transform (DWT) sub-bands as the feature to study in this novel approach. The Energetic Ratio (ER) served as a reference parameter to evaluate the EAC evolution, due to its proven efficiency in cardiac severity tracking. While the DWT-EAC algorithm results revealed that the EAC provides better results for the paper purposes, the One versus All Support Vector Machine (OVA-SVM) classifier affirmed the efficiency of the Entropy of Approximation Coefficients (EAC) for cardiac severity assessment and proved the accuracy of this novel approach.

Although telehealth, and in particular RPM, have demonstrated to drive many benefits, such as reduction in cost and hospital-acquired infections, previous research has shown many usability challenges when patients operate a medical device without supervision of a medical professional. To combat this issue, the Pi-CON methodology is applied to develop a novel sensor with the objective to continuously acquire a patient's vital signs from a distance, without the need to attach any markers or sensors to the patient, and with limited user interaction required. Pi-CON stands for passive, continuous and non-contact, and describes a way to improve the user experience for patients or caregivers that have a need to perform a vital signs measurement themselves, without the presence of a medical professional. The developed sensor utilises radar and optical sensing technologies and transmits acquired data to a cloud-based service where it can be viewed in near real-time by the patient or family members from anywhere via an intuitive user interface. This user interface, as well as the sensor itself were designed based on design needs and requirements to adhere to the user-centered design process. The development of the sensor, including utilised technologies, components, and the user interface are presented, including inspirations for future work.

The present work provides a comprehensive review of the literature on the mechanical properties and existing human tunica albuginea tissue testing methods. Assessments were completed on papers reporting experimental values of Young's modulus, tensile strength, puncture strength, stiffness, toughness, and strain at the ultimate tensile strength (UTS). A high degree of variability in the reported experimental values was found; Young's modulus ranged from 5 MPa to 118 MPa, and tensile strength went from 1.1 MPa to 6.1 MPa. A comparison of the variability of the reported experimental values for puncture strength, stiffness, toughness, and strain at the UTS could not be completed due to a lack of experimental results. This review discusses the pathophysiology and surgical treatment of erectile dysfunction and Peyronie's disease, variability in the existing reported mechanical properties, the impact of the variability of mechanical properties on in silico models and explores the absence of a standardised testing method as a possible reason for the variable in results. Finally, this work attempts to provide suggestions for standardising future mechanical testing of the tunica albuginea through minimising and reporting freeze/thaw cycling, noting the proximal/distal region of the cadaver tunica sample, reporting the orientation (o'clock position) of the cadaver tunica sample, and testing the cadaver tunica samples in bi-axial tension. Ultimately, standardising the testing methodologies of the tunica albuginea will provide higher confidence in reported mechanical property values.

Digital stethoscopes can enable the development of integrated artificial intelligence (AI) systems that can remove the subjectivity of manual auscultation, improve diagnostic accuracy, and compensate for diminishing auscultatory skills. Developing scalable AI systems can be challenging, especially when acquisition devices differ and thus introduce sensor bias. To address this issue, a precise knowledge of these differences, i.e., frequency responses of these devices, is needed, but the manufacturers often do not provide complete device specifications. In this study, we reported an effective methodology for determining the frequency response of a digital stethoscope and used it to characterise three common digital stethoscopes: Littmann 3200, Eko Core, and Thinklabs One. Our results show significant inter-device variability in that the frequency responses of the three studied stethoscopes were distinctly different. A moderate intra-device variability was seen when comparing two separate units of Littmann 3200. The study highlights the need for normalisation across devices for developing successful AI-assisted auscultation and provides a technical characterisation approach as a first step to accomplish it.

Endoscopic investigation plays a critical role in the diagnosis of gastrointestinal (GI) diseases. Since 2001, Wireless Capsule Endoscopy (WCE) has been available for small bowel exploration and is in continuous development. Over the last decade, WCE has achieved impressive improvements in areas such as miniaturisation, image quality and battery life. As a result, WCE is currently a very useful alternative to wired enteroscopy in the investigation of various small bowel abnormalities and has the potential to become the leading screening technique for the entire gastrointestinal tract. However, commercial solutions still have several limitations, namely incomplete examination and limited diagnostic capacity. These deficiencies are related to technical issues, such as image quality, motion estimation and power consumption management. Computational methods, based on image processing and analysis, can help to overcome these challenges and reduce both the time required by reviewers and human interpretation errors. Research groups have proposed a series of methods including algorithms for locating the capsule or lesion, assessing intestinal motility and improving image quality.In this work, we provide a critical review of computational vision-based methods for WCE image analysis aimed at overcoming the technological challenges of capsules. This article also reviews several representative public datasets used to evaluate the performance of WCE techniques and methods. Finally, some promising solutions of computational methods based on the analysis of multiple-camera endoscopic images are presented.

A first-level textile-based electrocardiogram (ECG) monitoring system referred to as "CardioS" (cardiac sensor) for continuous health monitoring applications is proposed in this study to address the demand for resource-constrained environments. and the signal quality assessment of a wireless CardioS was studied. The CardioS consists of a Lead-I ECG signal recorded wirelessly using silver-plated nylon woven (Ag-NyW) dry textile electrodes to compare the results of wired wearable Ag-NyW textile electrode-based ECG acquisition system and CardioS. The effect of prolonged usage of Ag-NyW dry electrodes on electrode impedance was tested in the current work. In addition, electrode half-cell potential was measured to validate the range of Ag-NyW dry electrodes for ECG signal acquisition. Further, the quality of signals recorded by the proposed wireless CardioS framework was evaluated and compared with clinical disposable (Ag-AgCl Gel) electrodes. The signal quality was assessed in terms of mean magnitude coherence spectra, signal cross-correlation, signal-to-noise-band ratio (S_band/N_band), crest factor, low and high band powers and power spectral density. The experimental results showed that the impedance was increased by 2.5-54.6% after six weeks of continuous usage. This increased impedance was less than 1 MΩ/cm², as reported in the literature. The half-cell potential of the Ag-NyW textile electrode obtained was 80 mV, sufficient to acquire the ECG signal from the human body. All the fidelity parameters measured by Ag-NyW textile electrodes were correlated with standard disposable electrodes. The cardiologists validated all the measurements and confirmed that the proposed framework exhibited good performance for ECG signal acquisition from the five healthy subjects. As a result of its low-cost architecture, the proposed CardioS framework can be used in resource-constrained environments for ECG monitoring.